Segment-wise channel equalization based data estimation

ABSTRACT

Data is estimated of a plurality of received spread spectrum signals. The plurality of received communications are received in a shared spectrum. The received communications are sampled to produce a received vector. The received vector is processed to produce a plurality of segments. Each segment is processed separately to estimate data of the received communications.

CROSS REFERENCE TO RELATED APPLICATION(S)

[0001] This application is a continuation of U.S. patent applicationSer. No. 10/153,112, filed May 22, 2002, which is incorporated byreference as if fully set forth

BACKGROUND

[0002] The invention generally relates to wireless communicationsystems. In particular, the invention relates to data detection in awireless communication system.

[0003]FIG. 1 is an illustration of a wireless communication system 10.The communication system 10 has base stations 12 ₁ to 12 ₅ (12) whichcommunicate with user equipments (UEs) 14 ₁ to 14 ₃ (14). Each basestation 12 has an associated operational area, where it communicateswith UEs 14 in its operational area.

[0004] In some communication systems, such as code division multipleaccess (CDMA) and time division duplex using code division multipleaccess (TDD/CDMA), multiple communications are sent over the samefrequency spectrum. These communications are differentiated by theirchannelization codes. To more efficiently use the frequency spectrum,TDD/CDMA communication systems use repeating frames divided into timeslots for communication. A communication sent in such a system will haveone or multiple associated codes and time slots assigned to it. The useof one code in one time slot is referred to as a resource unit.

[0005] Since multiple communications may be sent in the same frequencyspectrum and at the same time, a receiver in such a system mustdistinguish between the multiple communications. One approach todetecting such signals is joint detection. In joint detection, signalsassociated with all the UEs 14, users, are detected simultaneously.Approaches for joint detection include zero forcing block linearequalizers (ZF-BLE) and minimum mean square error (MMSE) BLE. Themethods to realize ZF-BLE or MMSE-BLE include Cholesky decompositionbased and fast Fourier transform (FFT) based approaches. Theseapproaches have a high complexity. The high complexity leads toincreased power consumption, which at the UE 14 results in reducedbattery life. Accordingly, it is desirable to have alternate approachesto detecting received data.

SUMMARY

[0006] Data is estimated of a plurality of received spread spectrumsignals. The plurality of received communications are received in ashared spectrum. The received communications are sampled to produce areceived vector. The received vector is processed to produce a pluralityof segments. Each segment is processed separately to estimate data ofthe received communications.

BRIEF DESCRIPTION OF THE DRAWING(S)

[0007]FIG. 1 is an illustration of a wireless spread spectrumcommunication system.

[0008]FIG. 2 is an illustration of a transmitter and a segment-wisechannel equalization data detection receiver.

[0009]FIG. 3 is an illustration of a communication burst andsegmentation of data fields of the communication burst.

[0010]FIG. 4 is a flow chart of a segment-wise channel equalization datadetection receiver.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT(S)

[0011]FIG. 2 illustrates a simplified transmitter 26 and receiver 28using a segment-wise channel equalization based data estimation in aTDD/CDMA communication system, although segment-wise channelequalization is applicable to other systems, such as frequency divisionduplex (FDD) CDMA or other hybrid time division multiple access(TDMA)/CDMA systems. In a typical system, a transmitter 26 is in each UE14 and multiple transmitting circuits 26 sending multiple communicationsare in each base station 12. The segment-wise channel equalizationreceiver 28 may be at a base station 12, UEs 14 or both.

[0012] The transmitter 26 sends data over a wireless radio channel 30. Adata generator 32 in the transmitter 26 generates data to becommunicated to the receiver 28. A modulation and spreading device 34spreads the data and makes the spread reference data time-multiplexedwith a midamble training sequence in the appropriate assigned time slotand codes for spreading the data, producing a communication burst orbursts.

[0013] A typical communication burst 16 has a midamble 20, a guardperiod 18 and two data fields 22, 24, as shown in FIG. 3. The midamble20 separates the two data fields 22, 24 and the guard period 18separates the communication bursts to allow for the difference inarrival times of bursts transmitted from different transmitters 26. Thetwo data fields 22, 24 contain the communication burst's data.

[0014] The communication burst(s) are modulated by a modulator 36 toradio frequency (RF). An antenna 38 radiates the RF signal through thewireless radio channel 30 to an antenna 40 of the receiver 28. The typeof modulation used for the transmitted communication can be any of thoseknown to those skilled in the art, such as quadrature phase shift keying(QPSK) or M-ary quadrature amplitude modulation (QAM).

[0015] The antenna 40 of the receiver 28 receives various radiofrequency signals. The received signals are demodulated by a demodulator42 to produce a baseband signal. The baseband signal is sampled by asampling device 43, such as one or multiple analog to digitalconverters, at the chip rate or a multiple of the chip rate of thetransmitted bursts to produce a received vector, r. The samples areprocessed, such as by a channel estimation device 44 and a segment-wisechannel equalization data detection device 46, in the time slot and withthe appropriate codes assigned to the received bursts. The channelestimation device 44 uses the midamble training sequence component inthe baseband samples to provide channel information, such as channelimpulse responses. The channel impulse responses can be viewed as amatrix, H. The channel information and spreading codes used by thetransmitter are used by the segment-wise channel equalization datadetection device 46 to estimate the transmitted data of the receivedcommunication bursts as soft symbols, d.

[0016] Although segment-wise channel equalization is explained using thethird generation partnership project (3GPP) universal terrestrial radioaccess (UTRA) TDD system as the underlying communication system, it isapplicable to other systems. That system is a direct sequence widebandCDMA (W-CDMA) system, where the uplink and downlink transmissions areconfined to mutually exclusive time slots.

[0017] The received communications can be viewed as a signal model perEquation 1.

r=Hs+n   Equation 1

[0018] r is the received vector. H is the channel response matrix. n isthe noise vector. s is the spread data vector, which is the convolutionof the spreading codes, C, and the data vector, d, as per Equation 2.

s=C d   Equation 2

[0019] Segment-wise channel equalization divides the received vector, r,into segments and processes each segment separately as shown in FIG. 4,step 50. FIG. 3 also illustrates segmentation of a communication burst.Each data field of the burst is N chips in length. The data fields aredivided into M segments 48 ₁₁-48 _(1M), 48 ₂₁-48 _(2M) (48). Thefollowing discussion uses a uniform segment length Y for each segment48, although the segments 48 based on the exact implementation may be ofdiffering lengths. Prior to processing each segment 48, Y1 chips priorto each segment are appended to the segment and Y2 chips after eachsegment 48 are appended to the segment 48, step 52. In general, theresulting length of each processed segment 48 is Z=Y+Y1+Y2.

[0020] For segments 48 ₁₂-48 _(1M-1), 48 ₂₂-48 _(2M-1) not on the endsof the data fields, Y1 and Y2 overlap with other segments 48. Sincenothing precedes the first segment 48 ₁₁ of the first data field 22, Y1chips prior to that segment are not taken. Segment-wise channelequalization may be performed on the Y+Y2 chips. For implementationpurposes, it may be desirable to have each segment 48 of a uniformlength. For the first segment 48 ₁₁, this may be accomplished bypadding, such as by zero padding, the beginning of the segment or byextending the chips analyzed at the tail end from Y2 to Y2+Y1. For thelast segment 48 _(1M) of the first data field 22, Y2 is the first Y2chips of the midamble 20. For the first segment 48 ₂₁ of the second datafield 24, Y1 extends into the midamble 20. For the last segment 48 _(2M)of the second data field 24, Y2 extends into the guard period 18.

[0021] Preferably, both Y1 and Y2 are at least the length of the impulseresponse W less one chip (W−1). The last chip's impulse response in eachsegment extends by W−1 chips into the next segment. Conversely, thefurthest chip's impulse response prior to a segment that extends intothat segment is W−1 chips ahead of the segment. Using W−1 chips prior tothe segment allows all the influence of all of the prior chips to beequalized out of the desired segment. Using W−1 chips after the segmentallows all the information (impulse response) for each chip of thesegment extending into the next segment to be used in the datadetection. It may be desirable to have Y1 or Y2 be longer than W−1 tofacilitate a specific implementation of segment-wise channelequalization. To illustrate, the length of Y1 and Y2 may be extended sothat a convenient length for a prime factor algorithm fast Fouriertransform can be utilized. This may also be accomplished by padding,such as by zero padding the extended postions.

[0022] Using the M extended segments, Equation 1 is rewritten asEquation 3 for each segment.

r _(i) =H _(s) s _(i) +n _(i), where i=1, . . . , M   Equation 3

[0023] H_(s) is the channel response matrix corresponding to thesegment. If each segment is of equal length, H_(s) is typically the samefor each segment.

[0024] Two approaches to solve Equation 3 use an equalization stagefollowed by a despreading stage. Each received vector segment, r_(i), isequalized, step 54. One equalization approach uses a minimum mean squareerror (MMSE) solution. The MMSE solution for each extended segment isper Equation 4.

ŝ _(i)=(H _(s) ^(H) H _(s)+σ² I _(s))⁻¹ H _(s) ^(H) r _(i)   Equation 4

[0025] σ² is the noise variance and I_(s) is the identity matrix for theextended matrix. (.)^(H) is the complex conjugate transpose operation orHermetian operation. Alternately, Equation 4 is written as Equation 5.

ŝ _(i) =R _(s) ⁻¹ H _(s) ^(H) r _(i)   Equation 5

[0026] R_(s) is defined per Equation 6.

R _(s) =H _(s) ^(H) H _(s)+σ² I _(s)   Equation 6

[0027] Using either Equation 4 or 5, a MMSE equalization of each segmentis obtained.

[0028] One approach to solve Equation 6 is by a fast Fourier transform(FFT) as per Equations 7 and 8.

R _(s) =D _(Z) ⁻¹ ΛD _(Z)=(1/P)D _(Z) *ΛD _(Z)   Equation 7

R _(s) ⁻¹ =D _(Z) ⁻¹Λ⁻¹ D _(Z)=(1/P)D _(Z) *Λ*D _(Z)   Equation 8

[0029] D_(Z) is the Z-point FFT matrix and Λ is the diagonal matrix,which has diagonals that are an FFT of the first column of a circulantapproximation of the R_(s) matrix. The circulant approximation can beperformed using any column of the R_(s) matrix. Preferably, a fullcolumn, having the most number of elements, is used.

[0030] In the frequency domain, the FFT solution is per Equation 9.$\begin{matrix}\begin{matrix}{{F\left( \underset{\_}{\hat{s}} \right)} = \frac{\sum\limits_{m = 1}^{M}\quad {{F\left( {\underset{\_}{h}}_{m} \right)}^{*} \otimes {F\left( {\underset{\_}{r}}_{m} \right)}}}{F\left( \underset{\_}{q} \right)}} \\{{{{where}\quad {F\left( \underset{\_}{x} \right)}} = {\sum\limits_{n = 0}^{P - 1}\quad {{x(n)}^{{- j}\frac{2\quad \pi \quad k\quad n}{N}}}}},{{{where}\quad k} = 0},1,\quad \ldots \quad,{P - 1}}\end{matrix} & {{Equation}\quad 9}\end{matrix}$

[0031] {circle over (x)} is the kronecker product. M is the samplingrate. M=1 is chip rate sampling and M=2 is twice the chip rate sampling.

[0032] After the Fourier transform of the spread data vector, F(ŝ), isdetermined, the spread data vector {circumflex over (s)} is determinedby taking an inverse Fourier transform. A second approach to solveEquation 6 is by Cholesky or approximate Cholesky decomposition.

[0033] Another solution for the equalization stage other than MMSE is aleast squares error (LSE) solution. The LSE solution for each extendedsegment is per Equation 10.

{circumflex over (s)} _(i)=(H _(s) ^(H) H _(s))⁻¹ H _(s) ^(H) r _(i)  Equation 10

[0034] After equalization, the first Y1 and the last Y2 chips arediscarded, step 56. As a result, {circumflex over (s)}_(i) becomes{tilde over (s)} _(i)·{tilde over (s)} _(i) is of length Y. To producethe data symbols {tilde over (d)} _(i), {tilde over (s)} _(i) isdespread per Equation 11, step 58.

{tilde over (d)} _(i)=C_(s) _(^(H)) {tilde over (s)} _(i)   Equation 11

[0035] C_(s) is the portion of the channel codes corresponding to thatsegment.

[0036] Alternately, the segments are recombined into an equalized spreaddata field {tilde over (s)} and the entire spread data field is despreadper Equation 12, step 58.

{tilde over (d)}=C^(H) {tilde over (s)}  Equation 12

[0037] Although segment-wise channel equalization based data estimationwas explained in the context of a typical TDD burst, it can be appliedto other spread spectrum systems. To illustrate for a FDD/CDMA system, aFDD/CDMA system receives communications over long time periods. As thereceiver 28 receives the FDD/CDMA communications, the receiver 28divides the samples into segments ŝ _(i) and segment-wise channelequalization is applied.

[0038] By breaking the received vector, r, into segments prior toprocessing, the complexity for the data detection is reduced. Toillustrate the complexity reduction, a data field of a TDD burst having1024 chips (N=1024) is used. Four different scenarios using a FFT/MMSEapproach to equalization are compared: a first scenario processes theentire data field of length 1024, a second scenario divides the entiredata field into two segments of length 512, a third scenario divides theentire data field into four segments of length 256 and a fourth scenariodivides the entire data field into eight segments of length 128. Forsimplicity, no overlap between the segments was assumed for thecomparison. In practice due to the overlap, the complexity for thesegmented approaches is slightly larger than indicated in the followingtables.

[0039] Table 1 illustrates the number of complex operations required toperform the data detection using Radix-2 FFTs. The table shows thenumber of Radix-2 and direct multiple operations required for eachscenario. TABLE 1 Number of Complex One Two Three Four OperationsSegment Segments Segments Segments Radix-2 1024 9216 8192 7168 DirectMultiply 1049K 524K 262K 131K

[0040] Table 2 compares the percentage of complexity of each scenariousing one segment as 100% complexity. The percentage of complexity isshow for both Radix-2 and direct multiple operations. TABLE 2 One TwoThree Four % Complexity Segment Segments Segments Segments Radix-2 100%90% 80%   70% Direct Multiply 100% 50% 25% 12.5%

[0041] For chip rate sampling, one F(h), one F(q), two F(r) and twoinverse FFTs are performed for each segment. For twice the chip ratesampling, two F(h), one F(q), four F(r) and two inverse FFTs areperformed for each segment. Table 3 illustrates the complexity ofRadix-2 operations at both the chip rate and twice the chip rate. TABLE3 Number of Complex One Two Three Four Operations Segment SegmentsSegments Segments Radix-2 60K 45K 36K 30K (Chip Rate) Radix-2 90K 68K54K 45K (Twice Chip Rate)

[0042] Table 4 shows the total complexity as a percentage for theRadix-2 operations for both chip rate and twice chip rate sampling.TABLE 4 One Two Three Four % Complexity Segment Segments SegmentsSegments Radix-2 100% 75% 60% 50% (Chip Rate) Radix-2 100% 76% 60% 50%(Twice Chip Rate)

[0043] As shown by the tables, in general, as the number of segmentsincreases, the overall complexity decreases. However, if the size of thesegments is decreased to far, such as to the length of the impulseresponse, due to the overlap between segments, the complexity increases.

[0044] To illustrate segment-wise channel equalization in a practicalsystem, a TDD burst type 2 is used. A similar segmentations can be usedfor other bursts, such as a burst type 1. A TDD burst type 2 has twodata fields of length 1104 (N=1104). The channel response for theseillustrations is of length 63 chips (W=63). Y1 and Y2 are set to W−1 or62 chips. The following are three potential segmentations, althoughother segmentations may be used.

[0045] The first segmentation divides each data field into two segmentsof length 552. With overlap between the segments, each segment is oflength 676 (Y+Y1+Y2). The second segmentation divides each data fieldinto three segments of length 368. With overlap between the segments,each segment is of length 492 (Y+Y1+Y2). The third segmentation divideseach data field into four segments of length 184. With overlap betweenthe segments, each segment is of length 308 (Y+Y1+Y2).

What is claimed is:
 1. A method for estimating data of a plurality ofreceived spread spectrum communications, the plurality of receivedspread spectrum communications received in a shared spectrum, the methodcomprising: sampling the received communications to produce a receivedvector; processing the received vector to produce a plurality ofsegments; and processing each segment separately to estimate data of thereceived communications.
 2. The method of claim 1 wherein the segmentscomprise overlapping portions of the received vector.
 3. The method ofclaim 2 wherein the processing each segment comprises equalizing eachsegment.
 4. The method of claim 3 wherein the overlapping portions ofthe received vector have a length of at least a length of an impulseresponse.
 5. The method of claim 3 wherein the processing each segmentcomprises despreading each equalized segment to recover data of thatsegment.
 6. The method of claim 3 further comprising combining theequalized segments and despreading the equalized combined segments torecover data of the received vector.
 7. The method of claim 3 whereinthe equalizing each segment uses a minimum mean square error model. 8.The method of claim 3 wherein the equalizing each segment comprisessolving a minimum mean square error model using fast Fourier transforms.9. The method of claim 3 wherein the equalizing each segment comprisessolving a minimum mean square error model using Cholesky decomposition.10. The method of claim 3 wherein the equalizing each segment comprisessolving a minimum mean square error model using approximate Choleskydecomposition.
 11. The method of claim 3 wherein the equalizing eachsegment uses a least squares error model.
 12. A user equipment forestimating data of a plurality of received spread spectrumcommunications, the plurality of received spread spectrum communicationsreceived in a shared spectrum, the user equipment comprising: a samplingdevice for sampling the received communications to produce a receivedvector; and a segment-wise channel equalization data detection devicefor processing the received vector to produce a plurality of segmentsand for processing each segment separately to estimate data of thereceived communications.
 13. The user equipment of claim 12 wherein thesegments comprise overlapping portions of the received vector.
 14. Theuser equipment of claim 13 wherein the processing each segment comprisesequalizing each segment.
 15. The user equipment of claim 14 wherein theoverlapping portions of the received vector have a length of at least alength of an impulse response.
 16. The user equipment of claim 14wherein the processing each segment comprises despreading each equalizedsegment to recover data of that segment.
 17. The user equipment of claim14 further comprising combining the equalized segments and despreadingthe equalized combined segments to recover data of the received vector.18. The user equipment of claim 14 wherein the equalizing each segmentuses a minimum mean square error model.
 19. The user equipment of claim14 wherein the equalizing each segment comprises solving a minimum meansquare error model using fast Fourier transforms.
 20. The user equipmentof claim 14 wherein the equalizing each segment comprises solving aminimum mean square error model using Cholesky decomposition.
 21. Theuser equipment of claim 14 wherein the equalizing each segment comprisessolving a minimum mean square error model using approximate Choleskydecomposition.
 22. The user equipment of claim 14 wherein the equalizingeach segment uses a least squares error model.
 23. A user equipment forestimating data of a plurality of received spread spectrumcommunications, the plurality of received spread spectrum communicationsreceived in a shared spectrum, the user equipment comprising: means forsampling the received communications to produce a received vector; meansfor processing the received vector to produce a plurality of segments;and means for processing each segment separately to estimate data of thereceived communications.
 24. The user equipment of claim 23 wherein thesegments comprise overlapping portions of the received vector.
 25. Theuser equipment of claim 24 wherein the processing each segment comprisesequalizing each segment.
 26. The user equipment of claim 25 wherein theoverlapping portions of the received vector have a length of at least alength of an impulse response..
 27. The user equipment of claim 25wherein the processing each segment comprises despreading each equalizedsegment to recover data of that segment.
 28. The user equipment of claim25 further comprising combining the equalized segments and despreadingthe equalized combined segments to recover data of the received vector.29. The user equipment of claim 25 wherein the equalizing each segmentuses a minimum mean square error model.
 30. The user equipment of claim25 wherein the equalizing each segment comprises solving a minimum meansquare error model using fast Fourier transforms.
 31. The user equipmentof claim 25 wherein the equalizing each segment comprises solving aminimum mean square error model using Cholesky decomposition.
 32. Theuser equipment of claim 25 wherein the equalizing each segment comprisessolving a minimum mean square error model using approximate Choleskydecomposition.
 33. The user equipment of claim 25 wherein the equalizingeach segment uses a least squares error model.
 34. A base station forestimating data of a plurality of received spread spectrumcommunications, the plurality of received spread spectrum communicationsreceived in a shared spectrum, the base station comprising: a samplingdevice for sampling the received communications to produce a receivedvector; and a segment-wise channel equalization data detection devicefor processing the received vector to produce a plurality of segmentsand for processing each segment separately to estimate data of thereceived communications.
 35. The base station of claim 34 wherein thesegments comprise overlapping portions of the received vector.
 36. Thebase station of claim 35 wherein the processing each segment comprisesequalizing each segment.
 37. The base station of claim 36 wherein theoverlapping portions of the received vector have a length of at least alength of an impulse response.
 38. The base station of claim 36 whereinthe processing each segment comprises despreading each equalized segmentto recover data of that segment.
 39. The base station of claim 36further comprising combining the equalized segments and despreading theequalized combined segments to recover data of the received vector. 40.The base station of claim 36 wherein the equalizing each segment uses aminimum mean square error model.
 41. The base station of claim 36wherein the equalizing each segment comprises solving a minimum meansquare error model using fast Fourier transforms.
 42. The base stationof claim 36 wherein the equalizing each segment comprises solving aminimum mean square error model using Cholesky decomposition.
 43. Thebase station of claim 36 wherein the equalizing each segment comprisessolving a minimum mean square error model using approximate Choleskydecomposition.
 44. The base station of claim 36 wherein the equalizingeach segment uses a least squares error model.
 45. A base station forestimating data of a plurality of received spread spectrumcommunications, the plurality of received spread spectrum communicationsreceived in a shared spectrum, the base station comprising: means forsampling the received communications to produce a received vector; meansfor processing the received vector to produce a plurality of segments;and means for processing each segment separately to estimate data of thereceived communications.
 46. The base station of claim 45 wherein thesegments comprise overlapping portions of the received vector.
 47. Thebase station of claim 46 wherein the processing each segment comprisesequalizing each segment.
 48. The base station of claim 47 wherein theoverlapping portions of the received vector have a length of at least alength of an impulse response.
 49. The base station of claim 47 whereinthe processing each segment comprises despreading each equalized segmentto recover data of that segment.
 50. The base station of claim 47further comprising combining the equalized segments and despreading theequalized combined segments to recover data of the received vector. 51.The base station of claim 47 wherein the equalizing each segment uses aminimum mean square error model.
 52. The base station of claim 47wherein the equalizing each segment comprises solving a minimum meansquare error model using fast Fourier transforms.
 53. The base stationof claim 47 wherein the equalizing each segment comprises solving aminimum mean square error model using Cholesky decomposition.
 54. Thebase station of claim 47 wherein the equalizing each segment comprisessolving a minimum mean square error model using approximate Choleskydecomposition.
 55. The base station of claim 47 wherein the equalizingeach segment uses a least squares error model.